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The availability of big data has greatly expanded opportunities to study society and human behaviour through the prism of computational analyses. The resulting field is known as computational social science and is defined by its interdisciplinary approaches. However, this type of cross-discipline work is intrinsically challenging, calling for the development of new collaborations and toolkits. In this Nature special collection of articles, we explore some of the fundamental questions and opportunities in computational social science.
This Perspective discusses the challenges for social science practices imposed by the ubiquity of algorithms and large-scale measurement and what should—and should not—be measured in societies pervaded by algorithms.
An epidemiological model that integrates fine-grained mobility networks illuminates mobility-related mechanisms that contribute to higher infection rates among disadvantaged socioeconomic and racial groups, and finds that restricting maximum occupancy at locations is especially effective for curbing infections.
An analysis of the search behaviour of recruiters on a Swiss online recruitment platform shows that jobseekers from minority ethnic groups are less likely to be contacted by recruiters, and also provides evidence of gender-based discrimination.
Insights into the interactions between pro- and anti-vaccination clusters on Facebook can enable policies and approaches that attempt to interrupt the shift to anti-vaccination views and persuade undecided individuals to adopt a pro-vaccination stance.
A model shows that human mobility is organized within hierarchical containers that coincide with familiar scales and that a power-law distribution emerges when movements between different containers are combined.
An analysis of worldwide data finds that human mobility has a hierarchical structure. A proposed model that accounts for such hierarchies reproduces differences in mobility behaviour across genders and levels of urbanization.
Understanding the dynamics of SARS-CoV-2 infections could help to limit viral spread. Analysing mobile-phone data to track human contacts at different city venues offers a way to model infection risks and explain infection disparities.